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#pragma once
#include <torch/csrc/autograd/function_hook.h>
#include <torch/csrc/python_headers.h>
#include <torch/csrc/utils/object_ptr.h>
namespace torch::dynamo::autograd {
class SwapSavedVariables;
} // namespace torch::dynamo::autograd
namespace torch::autograd {
struct PyFunctionTensorPreHook : public FunctionPreHook {
PyFunctionTensorPreHook(PyObject* dict, size_t value_idx);
~PyFunctionTensorPreHook() override;
variable_list operator()(const variable_list& values) override;
void compiled_args(torch::dynamo::autograd::CompiledNodeArgs& args) override;
PyObject* dict;
size_t value_idx;
};
struct PyFunctionPreHook : public FunctionPreHook {
PyFunctionPreHook(PyObject* dict);
~PyFunctionPreHook() override;
variable_list operator()(const variable_list& values) override;
void compiled_args(torch::dynamo::autograd::CompiledNodeArgs& args) override;
PyObject* dict;
};
struct PyFunctionPostHook : public FunctionPostHook {
PyFunctionPostHook(PyObject* dict);
~PyFunctionPostHook() override;
variable_list operator()(
const variable_list& outputs,
const variable_list& inputs) override;
void compiled_args(torch::dynamo::autograd::CompiledNodeArgs& args) override;
PyObject* dict;
};
// PyFunctionTensorPostAccGradHooks is a dictionary of PostAccumulateGradHooks,
// and it is understandable if you are confused by why it's a subclass. We are
// simply following the precedent of PyFunctionPreHook and PyFunctionPostHook
// above to easily enroll into existing infrastructure.
struct PyFunctionTensorPostAccGradHooks : public PostAccumulateGradHook {
PyFunctionTensorPostAccGradHooks(PyObject* dict);
~PyFunctionTensorPostAccGradHooks() override;
void operator()(const Variable& tensor) override;
void compiled_args(torch::dynamo::autograd::CompiledNodeArgs& args) override;
void apply_with_saved(
Variable& tensor,
torch::dynamo::autograd::SwapSavedVariables& saved) override;
PyObject* dict;
};
} // namespace torch::autograd
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